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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6842 条记录，以下是241-250 订阅

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Improvements in natural language understanding using deep learning 3

Improvements in natural language understanding using deep le...

引用

3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Cen, Xia Department of Chinese Language and Literature Northwest Minzu University Lanzhou730030 China

ISBN: (纸本)9781510680531

Natural Language Processing (NLP) is a growing area of artificial intelligence research with the goal of enabling computers to understand and interpret human language. In this study, we propose a new deep learning-based model that aims to improve machine understanding of natural language. We implemented a model that combines Recurrent Neural Networks (RNN) and Attention Mechanisms and tested it on multiple datasets, including sentiment analysis and text summarization. Experimental results show that our model outperforms existing techniques on several tasks. © 2024 SPIE.

关键词： Recurrent neural networks

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Flight test parameter state prediction technology based on machine learning 3

Flight test parameter state prediction technology based on m...

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Yang, Zhe Chinese Flight Test Establishment Yanliang District Shaanxi Province Xi'an China Northwestern Polytechnical University Shaanxi Province Xi'an China

ISBN: (纸本)9781510680449

According to the demand of flight test safety early warning, machine learning technology is adopted to carry out the research of test flight parameter state prediction technology. The relevant principles and procedures are introduced, including test flight data pre-processing, intelligent prediction model training, test flight parameter status online prediction and correction, etc. The test results show that the data of this method has a good fit and a good generalization performance, which provides a technical basis for test flight danger prediction and safety early warning. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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Building a Cancer Type Prediction Model Based on machine learning Algorithms by Analyzing the Origin Genes of Multiple Cancers Including Prostate Cancer 3

Building a Cancer Type Prediction Model Based on Machine Lea...

引用

3rd international Joint conference on Information and Communication engineering (JCICE)

作者： Zhang, Beier Guo, Xintian Fan, Qi Wang, Tingting Xia, Sihan Meng, Fanyi Huaibei Normal Univ Sch Comp Sci & Technol Huaibei Peoples R China

ISBN: (纸本)9798350388732;9798350388725

This study identified common genes associated with various primary cancers, including prostate cancer, and established a predictive model to accurately forecast the likelihood of cancer occurrence and its specific type. The aim is to offer physicians assistance in treatment decisions and meticulous follow-up, ultimately improving patient prognosis. Tumor sample data were collected from TCGA database, and differential expression analysis was employed for feature gene selection. machine learning models were constructed to trace the origin of cancer genes. The results revealed 663 differentially expressed genes exhibiting characteristic expression in prostate cancer, squamous cell lung cancer, thyroid cancer, clear cell renal cell carcinoma, and bladder urothelial carcinoma. Logistic regression demonstrated superior stability and performance, with an average accuracy increase of 4% compared to other models. Therefore, precise prediction of cancer occurrence and its specific type based on gene expression status can be achieved, providing robust support for physicians' diagnosis and treatment decisions. This approach has the potential to enhance patient prognosis by enabling accurate predictions and targeted interventions.

关键词： TCGA database multi-cancer prediction differential expression analysis machine learning patient prediction model

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machine learning based Man-in-the-Middle Attack Prediction 3

Machine Learning based Man-in-the-Middle Attack Prediction

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Rao, K. Venkateswara Akshaya, B. Renu Satvik, G. Gershon Rohith, B. Lahari, G. Chakrika Bala Vadlamudi India

ISBN: (纸本)9798350375190

In today's digital world, people are using networks and communication channels. With this increase, there's also a rise in different kinds of attacks aimed at online users. One of the most notable threats is the man-in-the-middle attack. Detecting this type of attack is tough because the attacker tries to stay hidden. They aim to intercept important user information without being noticed. The proposed system uses machine learning techniques and provides a solution by predicting when man-in-the-middle attacks might happen. Here, the prediction is done by analyzing data from both general and wireless network monitoring tools. Additionally, this study makes it easier to understand the data by presenting it in different formats and enabling a thorough risk analysis. © 2024 IEEE.

关键词： machine learning

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machine learning Algorithm for Predicting Soil Classification using Smart Agriculture 3

Machine Learning Algorithm for Predicting Soil Classificatio...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Ponkumar, D. David Neels Kumar, Kolipaka Pushpa Krupa Himesh Ramesh, S. Kumar, J. Arun Nallasivan, G. Jayachandra, A. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396157

The advent of Internet of Things (IoT) technology and data analytics has brought about a significant transformation in contemporary agricultural practices, sometimes referred to as smart agriculture. One of the fundamental uses of this technology is the prediction of soil classification, a critical factor in the optimization of crop management strategies and the allocation of resources. The present work investigates the use of machine learning techniques to achieve precise and efficient soil categorization in the context of smart agriculture. The study utilizes a comprehensive dataset consisting of several soil characteristics, including pH levels, moisture content, texture, and nutrient composition. These attributes were obtained via the use of Internet of Things (IoT) sensors and Unmanned Aerial Vehicles (UAVs). Several machine learning techniques, such as Random Forest, Support Vector machine, and Neural Networks, are assessed in terms of the classification accuracy in identifying soil types using the given characteristics. The findings indicate that machine learning models has the capability to accurately forecast soil categorization, hence enabling the use of precision agricultural techniques. These models assist farmers in making informed choices based on data analysis pertaining to crop selection, irrigation, and fertilization, resulting in enhanced agricultural productivity and the adoption of sustainable resource management practices. Additionally, this research investigates the interpretability of the chosen machine learning models, with the aim of ensuring that farmers are able to grasp and place faith in the forecasts. The proper implementation of smart agricultural systems also involves addressing ethical problems related to data privacy and security. This study makes a valuable contribution to the progress of sustainable agriculture via the use of machine learning and Internet of Things (IoT) technology. By leveraging these technologies, the research a

关键词： Soils

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A Comparative Study of machine learning Models for Network Traffic Classification in the IoT Landscape 3

A Comparative Study of Machine Learning Models for Network T...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Thakur, Divyanshu Sah, Srijan Ailsinghani, Priyansh Ranga, Virender Delhi Technological University Department of Information Technology New Delhi India

ISBN: (纸本)9798350375190

Network security is a top priority in today's digital economy, where intrusions grow in sophistication and size. To safeguard network infrastructure and data, it is critical to discern between legitimate and malicious network traffic. machine learning classification models have proven useful in this task, with decision trees, random forests, and support vector machines among the most common options. This study analyzes six machine learning techniques for determining whether network data is benign or malicious. The performance evaluation is based on the accuracy, precision, F1-score, AUC ROC score, and computational efficiency of the latest IoT dataset, the TII-SSRC-23 dataset from Kaggle. The findings shed light on each technique's strengths and drawbacks, as well as recommendations for choosing the best approach for network traffic classification. © 2024 IEEE.

关键词： Network security

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Construction of Diabetes Prediction Model Based on machine learning 3

Construction of Diabetes Prediction Model Based on Machine L...

引用

3rd international conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Xinping, Shang Ke, Jian Xuliang, Yang Yi, Wang Artificial Intelligence Dongguan City University Guangdong Dongguan China

ISBN: (纸本)9798331529222

Diabetes is a global epidemic of chronic diseases;early identification of high-risk groups can effectively reduce the incidence of diabetes and reduce the risk of complications. In recent years, the predictive model based on machine learning has been gradually applied to the early screening and prediction of diabetes due to its efficient data processing ability. In this study, a diabetes prediction model was constructed by analyzing and comparing common machine learning algorithms such as logistic regression, K-nearest neighbor, decision tree, random forest, neural network and XGBoost. The experimental results show that random forest and XGBoost have significant advantages in predicting diabetes mellitus with high prediction accuracy. This study provides an effective tool for clinical applications and provides a reference for the development of future medical prediction models based on machine learning. ©2024 IEEE.

关键词： Prediction models

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Research on the Application of ESG Index Quantitative Analysis Based on machine learning Algorithm in Financial engineering 3

Research on the Application of ESG Index Quantitative Analys...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Huo, Yucheng Sun, Chujie Guo, Jiangshan Hebei University of Economics and Business Hebei Shijiazhuang050061 China

ISBN: (纸本)9798331519032

The lack of standardization and transparency of ESG (environment, society and governance) data limits the effectiveness of traditional quantitative analysis. In this paper, a quantitative analysis model of ESG indicators based on multi-source data fusion and machine learning algorithm is designed. The model integrates multi-dimensional indicator data of environment, society and governance. Through data cleaning, standardization and normalization, random forest (RF) is used as the main algorithm to predict ESG comprehensive score, and the Stacking integration method combined with support vector machine (SVM) and logistic regression (LR) is tried to further improve the prediction accuracy. The empirical analysis selects 500 large listed companies in the world as the research object, covering many industries. The results show that the integrated model has obvious advantages in forecasting the comprehensive score of ESG, the mean square error (MSE) is significantly reduced, and the determination coefficient is significantly improved, indicating that the model can more accurately reflect the actual changes of ESG score. In addition, by analyzing the model performance of different industries, it is found that the prediction error of science and technology and health care industries is small, while the prediction error of energy and manufacturing industries is relatively high. The research in this paper not only provides new ideas and methods for the quantitative analysis of ESG indicators, but also verifies the potential of machine learning algorithm in improving ESG evaluation accuracy and prediction ability through empirical analysis. © 2024 IEEE.

关键词： Random forests

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Radar emitter recognition method based on kernel extreme learning machine optimized by sparrow search algorithm 3

Radar emitter recognition method based on kernel extreme lea...

引用

3rd international conference on Computer Technology, Information engineering, and Electron Materials, CTIEEM 2023

作者： Fan, Bin Dai, Wenrui Li, Jun Huang, Lingling Department of Information Engineering PLA Army Academy of Artillery and Air Defense Hefei China Department of Foundation PLA Army Academy of Artillery and Air Defense Hefei China

ISBN: (纸本)9781510672925

Aiming at the problem of low radar emitter recognition rate in electronic reconnaissance under low signal-to-noise ratio (SNR) conditions, a radar emitter recognition method based on kernel extreme learning machine optimized by sparrow search algorithm is proposed. By constructing a radar database, the kernel extreme learning machine optimized by sparrow search algorithm is used to identify radar data under different noise conditions, and is compared with the traditional kernel extreme learning machine and support vector machine method. The results show that the recognition accuracy of the proposed method is higher than that of other methods in different noise conditions. © 2024 SPIE.

关键词： learning algorithms

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Traditional Work Station System with AI based Crypto Mining 3

Traditional Work Station System with AI based Crypto Mining

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Vanitha, M. Harivanth, A. Govardhan, K. Saveetha Engineering College India

ISBN: (纸本)9798331532420

This research explores the feasibility of AI-powered cryptocurrency mining on traditional workstations. Traditional workstations, while widely available, face challenges in terms of computational efficiency and energy consumption compared to specialized hardware like ASICs and GPUs. This project aims to address these challenges by leveraging machine learning techniques to optimize mining performance. By analyzing real-time data such as hash rates, power usage, and network difficulty, the system will dynamically adjust mining parameters to maximize profitability while minimizing resource consumption. Furthermore, the project will investigate the use of reinforcement learning to enable the system to continuously adapt to changing mining conditions and optimize its performance over time. This approach aims to democratize cryptocurrency mining by making it accessible to users with traditional workstations, enabling them to participate in a sustainable and efficient manner. © 2024 IEEE.

关键词： Self-supervised learning

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